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Record W2086251045 · doi:10.1179/174327806x139081

Investigation of inhibitive effects of phosphonic acids on corrosion of iron in 3% sodium chloride solution

2006· article· en· W2086251045 on OpenAlexfundno aff
H. Amar, J. Benzakour, Ahmed Derja, Didier Villemin, Bernard Moreau, Thierry Braisaz

Bibliographic record

VenueCorrosion Engineering Science and Technology The International Journal of Corrosion Processes and Corrosion Control · 2006
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsnot available
FundersAgence Universitaire de la Francophonie
KeywordsTafel equationChemistryNuclear chemistryCorrosionFourier transform infrared spectroscopyNuclear magnetic resonance spectroscopyChlorideLangmuir adsorption modelInfrared spectroscopySpectroscopyAdsorptionInorganic chemistrySodiumOrganic chemistryElectrochemistryPhysical chemistryChemical engineering

Abstract

fetched live from OpenAlex

Thiomorpholin-4-ylmethyl-phosphonic acid (TMPA) and morpholin-4-methyl-phosphonic acid (MPA) have been synthesised using a microwave technique. The crude product obtained was purified and characterised by nuclear magnetic resonance spectroscopy (1H NMR,13C NMR, 31P NMR) and infrared spectroscopy. The inhibition efficiencies of the synthesised compounds have been studied by Tafel polarisation curve determinations and weight loss techniques. The comparison of the results with those reported previously for piperidin-1-yl-phosphonic acid showed that TMPA and MPA were better inhibitors. The addition of these compounds to aggressive media was accompanied by a decrease of the corrosion current density and a corresponding reduction of the corrosion rate. The inhibition efficiency increased with increasing inhibitor concentration. It reached 99·6 and 96·3% at a concentration of 5 × 10−3 M for TMPA and MPA respectively. The adsorption of the inhibitors was found to follow the Langmuir's isotherm. However, the determination of the film chemical nature required the use of Fourier transform infrared spectroscopy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.790

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.004
GPT teacher head0.207
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueCorrosion Engineering Science and Technology The International Journal of Corrosion Processes and Corrosion ControlSame topicCorrosion Behavior and InhibitionFrench-language works237,207